"We assess nonparametric kernel density regression as a technique for estimating mortgage loan prepayments - one of the key components in pricing highly volatile mortgage-backed securities and their derivatives. The highly non-linear and so-called """"irr ational"""" behavior of the prepayment function lends itself well to an estimator that is free of both functional and distributional assumptions. The technique is shown to exhibit superior out-of-sample predictive ability compared to both proportional hazards and proprietary practitioner models. Moreover, the best kernel model provides this improved predictive power utilizing a more parsimonious specification in terms of both data and covariates. We conclude that the technique may prove useful in other financial modeling applications, such as default modeling, and other derivative pricing problems where highly non-linear relationships and optionality exist."